The document describes an OLTP database created for a construction company to store ongoing and closed project data in third normal form. An ETL process was developed using SSIS to load data from Excel spreadsheets and XML files into the database tables. This ETL package was combined with database backup, shrink, and index rebuild processes into a single job scheduled to run regularly via SQL Server Agent. The document includes diagrams and details of the database structure and various SSIS packages developed for the ETL load processes.
Case Analysis of Molycorp: Financing the Production of Rare Earth Minerals”Rifat Ahsan
This document is a report on financing options for Molycorp's Project Phoenix, which aims to renovate and expand its rare earth mineral mining facility. Molycorp needs additional financing to complete the project. The report analyzes three alternatives: issuing straight debt (bonds), convertible debt, or common stock. Simulation and sensitivity analyses are conducted to estimate price ranges under each alternative and determine the most sensitive variables. A recommendation is made on the optimal financing approach.
Iceberg provides capabilities beyond traditional partitioning of data in Spark/Hive. It allows updating or deleting individual rows without rewriting partitions through mutable row operations (MOR). It also supports ACID transactions through versions, faster queries through statistics and sorting, and flexible schema changes. Iceberg manages metadata that traditional formats like Parquet do not, enabling these new capabilities. It is useful for workloads that require updating or filtering data at a granular record level, managing data history through versions, or frequent schema changes.
Tutoriel sous forme d'exercices pour découvrir le sparql endpoint mis à disposition par la plateforme HAL, archive ouverte d'article scientifiques de toutes disciplines des institutions de recherches françaises. Attention ! Ce tutoriel a pour pré-requis la connaissance du langage de requêtes SPARQL.
The document provides guidance on loading and unloading data from Snowflake using the COPY command. It discusses best practices for file formats, creating tables, loading data from CSV and JSON files located in an S3 bucket, validating data files before loading, and splitting large files into smaller files for improved load performance. Recommendations are provided for debugging data load issues, including using different ON_ERROR parameters to skip files or continue loading on errors.
Oracle Product Hub Cloud: A True Enterprise Product Master SolutionKPIT
Thermo Fisher Scientific is looking to implement an enterprise product master solution to manage their large and growing product data in a centralized and scalable way. They were facing challenges with synchronization between their PLM and ERP systems and inconsistent product data models across business units. Oracle Product Hub Cloud was selected as it provides comprehensive product data management functionality including BOM and where-used capabilities needed to support their supply chain. It also enables supplier collaboration and integration with various Oracle and non-Oracle systems. The presentation discusses Thermo Fisher's vision and drivers for the solution as well as lessons learned from Pella Corporation's implementation.
ETL extracts raw data from sources, transforms it on a separate server, and loads it into a target database. ELT loads raw data directly into a data warehouse, where data cleansing, enrichment, and transformations occur. While ETL has been used longer and has more supporting tools, ELT allows for faster queries, greater flexibility, and takes advantage of cloud data warehouse capabilities by performing transformations within the warehouse. However, ELT can present greater security risks and increased latency compared to ETL.
Tony Gibbs gave a presentation on Amazon Redshift covering its history, architecture, concepts, and parallelism. The presentation included details on Redshift's cluster architecture, node components, storage design, data distribution styles, and terminology. It also provided a deep dive on parallelism in Redshift, explaining how queries are compiled and executed through streams, segments, and steps to enable massively parallel processing across nodes.
The document describes an OLTP database created for a construction company to store ongoing and closed project data in third normal form. An ETL process was developed using SSIS to load data from Excel spreadsheets and XML files into the database tables. This ETL package was combined with database backup, shrink, and index rebuild processes into a single job scheduled to run regularly via SQL Server Agent. The document includes diagrams and details of the database structure and various SSIS packages developed for the ETL load processes.
Case Analysis of Molycorp: Financing the Production of Rare Earth Minerals”Rifat Ahsan
This document is a report on financing options for Molycorp's Project Phoenix, which aims to renovate and expand its rare earth mineral mining facility. Molycorp needs additional financing to complete the project. The report analyzes three alternatives: issuing straight debt (bonds), convertible debt, or common stock. Simulation and sensitivity analyses are conducted to estimate price ranges under each alternative and determine the most sensitive variables. A recommendation is made on the optimal financing approach.
Iceberg provides capabilities beyond traditional partitioning of data in Spark/Hive. It allows updating or deleting individual rows without rewriting partitions through mutable row operations (MOR). It also supports ACID transactions through versions, faster queries through statistics and sorting, and flexible schema changes. Iceberg manages metadata that traditional formats like Parquet do not, enabling these new capabilities. It is useful for workloads that require updating or filtering data at a granular record level, managing data history through versions, or frequent schema changes.
Tutoriel sous forme d'exercices pour découvrir le sparql endpoint mis à disposition par la plateforme HAL, archive ouverte d'article scientifiques de toutes disciplines des institutions de recherches françaises. Attention ! Ce tutoriel a pour pré-requis la connaissance du langage de requêtes SPARQL.
The document provides guidance on loading and unloading data from Snowflake using the COPY command. It discusses best practices for file formats, creating tables, loading data from CSV and JSON files located in an S3 bucket, validating data files before loading, and splitting large files into smaller files for improved load performance. Recommendations are provided for debugging data load issues, including using different ON_ERROR parameters to skip files or continue loading on errors.
Oracle Product Hub Cloud: A True Enterprise Product Master SolutionKPIT
Thermo Fisher Scientific is looking to implement an enterprise product master solution to manage their large and growing product data in a centralized and scalable way. They were facing challenges with synchronization between their PLM and ERP systems and inconsistent product data models across business units. Oracle Product Hub Cloud was selected as it provides comprehensive product data management functionality including BOM and where-used capabilities needed to support their supply chain. It also enables supplier collaboration and integration with various Oracle and non-Oracle systems. The presentation discusses Thermo Fisher's vision and drivers for the solution as well as lessons learned from Pella Corporation's implementation.
ETL extracts raw data from sources, transforms it on a separate server, and loads it into a target database. ELT loads raw data directly into a data warehouse, where data cleansing, enrichment, and transformations occur. While ETL has been used longer and has more supporting tools, ELT allows for faster queries, greater flexibility, and takes advantage of cloud data warehouse capabilities by performing transformations within the warehouse. However, ELT can present greater security risks and increased latency compared to ETL.
Tony Gibbs gave a presentation on Amazon Redshift covering its history, architecture, concepts, and parallelism. The presentation included details on Redshift's cluster architecture, node components, storage design, data distribution styles, and terminology. It also provided a deep dive on parallelism in Redshift, explaining how queries are compiled and executed through streams, segments, and steps to enable massively parallel processing across nodes.
Webinar: Secrets of ClickHouse Query Performance, by Robert HodgesAltinity Ltd
From webinars September 11 and September 17, 2019
ClickHouse is famous for speed. That said, you can almost always make it faster! This webinar uses examples to teach you how to deduce what queries are actually doing by reading the system log and system tables. We'll then explore standard ways to increase query speed: data types and encodings, filtering, join reordering, skip indexes, materialized views, session parameters, to name just a few. In each case we'll circle back to query plans and system metrics to demonstrate changes in ClickHouse behavior that explain the boost in performance. We hope you'll enjoy the first step to becoming a ClickHouse performance guru!
Speaker Bio:
Robert Hodges is CEO of Altinity, which offers enterprise support for ClickHouse. He has over three decades of experience in data management spanning 20 different DBMS types. ClickHouse is his current favorite. ;)
The document discusses how data is a strategic asset for organizations and how moving data to the cloud can unlock innovation and accelerate change. It emphasizes that businesses need a data strategy to ensure data is managed and used as an asset. This involves defining requirements for data quality, quantity, and sources. Migrating data to the cloud allows businesses to realize benefits like cost savings, build analytics capabilities, and gain new insights from data lakes, IoT, and machine learning. Case studies provide examples of how companies have benefited from improved data strategies and analytics.
The document provides an introduction to Apache Storm, an open-source distributed real-time computation system. It outlines the core concepts of Storm including topologies, spouts, bolts, streams and tuples. Spouts are sources of streams, while bolts process input streams and produce output streams. Topologies define the logic of an application as a graph of operators and streams. The document also discusses Storm's architecture, guaranteed processing, usage examples at companies like Twitter and Yahoo, and comparisons to other frameworks.
High-Performance Advanced Analytics with Spark-AlchemyDatabricks
Pre-aggregation is a powerful analytics technique as long as the measures being computed are reaggregable. Counts reaggregate with SUM, minimums with MIN, maximums with MAX, etc. The odd one out is distinct counts, which are not reaggregable.
Traditionally, the non-reaggregability of distinct counts leads to an implicit restriction: whichever system computes distinct counts has to have access to the most granular data and touch every row at query time. Because of this, in typical analytics architectures, where fast query response times are required, raw data has to be duplicated between Spark and another system such as an RDBMS. This talk is for everyone who computes or consumes distinct counts and for everyone who doesn’t understand the magical power of HyperLogLog (HLL) sketches.
We will break through the limits of traditional analytics architectures using the advanced HLL functionality and cross-system interoperability of the spark-alchemy open-source library, whose capabilities go beyond what is possible with OSS Spark, Redshift or even BigQuery. We will uncover patterns for 1000x gains in analytic query performance without data duplication and with significantly less capacity.
We will explore real-world use cases from Swoop’s petabyte-scale systems, improve data privacy when running analytics over sensitive data, and even see how a real-time analytics frontend running in a browser can be provisioned with data directly from Spark.
Managing Millions of Tests Using DatabricksDatabricks
Databricks Runtime is the execution environment that powers millions of VMs running data engineering and machine learning workloads daily in Databricks. Inside Databricks, we run millions of tests per day to ensure the quality of different versions of Databricks Runtime. Due to the large number of tests executed daily, we have been continuously facing the challenge of effective test result monitoring and problem triaging. In this talk, I am going to share our experience of building the automated test monitoring and reporting system using Databricks. I will cover how we ingest data from different data sources like CI systems and Bazel build metadata to Delta, and how we analyze test results and report failures to their owners through Jira. I will also show you how this system empowers us to build different types of reports that effectively track the quality of changes made to Databricks Runtime.
Running Mission Critical Workload for Financial Services Institutions on AWSAmazon Web Services
Capital One, Monzo Bank, and the National Australia Bank are examples of financial services customers running mission critical workloads on AWS. AWS provides security, reliability, performance, experience, and scale needed for these workloads. Mission critical workloads require high availability, security, and resilience. On AWS, customers can build fault-tolerant and secure architectures across multiple availability zones for their critical applications and data.
With the rise of the Internet of Things (IoT) and low-latency analytics, streaming data becomes ever more important. Surprisingly, one of the most promising approaches for processing streaming data is SQL. In this presentation, Julian Hyde shows how to build streaming SQL analytics that deliver results with low latency, adapt to network changes, and play nicely with BI tools and stored data. He also describes how Apache Calcite optimizes streaming queries, and the ongoing collaborations between Calcite and the Storm, Flink and Samza projects.
This talk was given Julian Hyde at Apache Big Data conference, Vancouver, on 2016/05/09.
Near Real-Time Data Warehousing with Apache Spark and Delta LakeDatabricks
Timely data in a data warehouse is a challenge many of us face, often with there being no straightforward solution.
Using a combination of batch and streaming data pipelines you can leverage the Delta Lake format to provide an enterprise data warehouse at a near real-time frequency. Delta Lake eases the ETL workload by enabling ACID transactions in a warehousing environment. Coupling this with structured streaming, you can achieve a low latency data warehouse. In this talk, we’ll talk about how to use Delta Lake to improve the latency of ingestion and storage of your data warehouse tables. We’ll also talk about how you can use spark streaming to build the aggregations and tables that drive your data warehouse.
Azure File Share and File Sync guide (Beginners Edition)Naseem Khoodoruth
Azure File Share and File Sync guide (Beginners Edition)
Option to have a file server on premise for caching or access the storage from your local desktop (Windows 10)
#azure #fileserver
As cloud computing continues to gather speed, organizations with years’ worth of data stored on legacy on-premise technologies are facing issues with scale, speed, and complexity. Your customers and business partners are likely eager to get data from you, especially if you can make the process easy and secure.
Challenges with performance are not uncommon and ongoing interventions are required just to “keep the lights on”.
Discover how Snowflake empowers you to meet your analytics needs by unlocking the potential of your data.
Agenda of Webinar :
~Understand Snowflake and its Architecture
~Quickly load data into Snowflake
~Leverage the latest in Snowflake’s unlimited performance and scale to make the data ready for analytics
~Deliver secure and governed access to all data – no more silos
ClickHouse Features for Advanced Users, by Aleksei MilovidovAltinity Ltd
This document summarizes key features for advanced users of ClickHouse, an open-source column-oriented database management system. It describes sample keys that can be defined in MergeTree tables to generate instant reports on large customer data. It also summarizes intermediate aggregation states, consistency modes, and tools for processing data without a server like clickhouse-local.
Why I quit Amazon and Build the Next-gen Streaming SystemYingjun Wu
Yingjun Wu quit their job at Amazon to build a next-generation streaming system called RisingWave that is designed to be cloud-native with a managed service, SQL-oriented interface, and cloud-native architecture in order to reduce operation, development, and performance costs compared to traditional streaming systems. RisingWave aims to provide a streaming database with a simple interface and optimized architecture that fully leverages cloud resources.
HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2017. It is the biggest and most exciting milestone release from the Apache community after 1.0. HBase-2.0 contains a large number of features that is long time in the development, some of which include rewritten region assignment, perf improvements (RPC, rewritten write pipeline, etc), async clients, C++ client, offheaping memstore and other buffers, Spark integration, shading of dependencies as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths.
Speaker
Ankit Singhal, Member of Technical Staff, Hortonworks
Benchmark MinHash+LSH algorithm on SparkXiaoqian Liu
This document summarizes benchmarking the MinHash and Locality Sensitive Hashing (LSH) algorithm for calculating pairwise similarity on Reddit post data in Spark. The MinHash algorithm was used to reduce the dimensionality of the data before applying LSH to further reduce dimensionality and find similar items. Benchmarking showed that MinHash+LSH was significantly faster than a brute force approach, calculating similarities in 7.68 seconds for 100k entries compared to 9.99 billion seconds for brute force. Precision was lower for MinHash+LSH at 0.009 compared to 1 for brute force, but recall was higher at 0.036 compared to vanishingly small for brute force. The techniques were also applied to a real-time streaming
Tableau Tutorial for Data Science | EdurekaEdureka!
YouTube Link:https://youtu.be/ZHNdSKMluI0
Edureka Tableau Certification Training: https://www.edureka.co/tableau-certification-training
This Edureka's PPT on "Tableau for Data Science" will help you to utilize Tableau as a tool for Data Science, not only for engagement but also comprehension efficiency. Through this PPT, you will learn to gain the maximum amount of insight with the least amount of effort.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration.
This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.
Data driven organizations can be challenged to deliver new and growing business intelligence requirements from existing data warehouse platforms, constrained by lack of scalability and performance. The solution for customers is a data warehouse that scales for real-time demands and uses resources in a more optimized and cost-effective manner. Join Snowflake, AWS and Ask.com to learn how Ask.com enhanced BI service levels and decreased expenses while meeting demand to collect, store and analyze over a terabyte of data per day. Snowflake Computing delivers a fast and flexible elastic data warehouse solution that reduces complexity and overhead, built on top of the elasticity, flexibility, and resiliency of AWS.
Join us to learn:
• Learn how Ask.com eliminates data redundancy, and simplifies and accelerates data load, unload, and administration
• Learn how to support new and fluid data consumption patterns with consistently high performance
• Best practices for scaling high data volume on Amazon EC2 and Amazon S3
Who should attend: CIOs, CTOs, CDOs, Directors of IT, IT Administrators, IT Architects, Data Warehouse Developers, Database Administrators, Business Analysts and Data Architects
This document provides an overview of the Elastic Stack including Elasticsearch, Logstash, Kibana, and Beats. It describes how each component works, key terminology, installation and configuration steps. It also demonstrates how to integrate the Elastic Stack for log analytics and security information and event management (SIEM) use cases including sending logs from Auditbeat, configuring file integrity monitoring, and alerting on log events using Elastalert.
Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
Dynamic Partition Pruning in Apache SparkDatabricks
In data analytics frameworks such as Spark it is important to detect and avoid scanning data that is irrelevant to the executed query, an optimization which is known as partition pruning. Dynamic partition pruning occurs when the optimizer is unable to identify at parse time the partitions it has to eliminate. In particular, we consider a star schema which consists of one or multiple fact tables referencing any number of dimension tables. In such join operations, we can prune the partitions the join reads from a fact table by identifying those partitions that result from filtering the dimension tables. In this talk we present a mechanism for performing dynamic partition pruning at runtime by reusing the dimension table broadcast results in hash joins and we show significant improvements for most TPCDS queries.
The JavaScript InfoVis Toolkit provides open source interactive data visualization tools using JSON data formats, including area charts, sunbursts, hyper trees, space trees, and more to help companies visualize hierarchical and quantitative data, though some coding experience is required as it is a library rather than an application.
Stellmach.2011.designing gaze supported multimodal interactions for the explo...mrgazer
This document describes research into designing gaze-supported multimodal interactions for exploring large image collections. Specifically, it investigates combining eye gaze with a touch-and-tilt mobile device or keyboard to control an adaptive fisheye lens visualization. User interviews were conducted to understand how people would want to use such input combinations for browsing images. Based on this feedback, a prototype system was developed using a SpringLens technique with gaze and a touch device. A user study provided insights into how well the gaze-supported interaction techniques were experienced.
Webinar: Secrets of ClickHouse Query Performance, by Robert HodgesAltinity Ltd
From webinars September 11 and September 17, 2019
ClickHouse is famous for speed. That said, you can almost always make it faster! This webinar uses examples to teach you how to deduce what queries are actually doing by reading the system log and system tables. We'll then explore standard ways to increase query speed: data types and encodings, filtering, join reordering, skip indexes, materialized views, session parameters, to name just a few. In each case we'll circle back to query plans and system metrics to demonstrate changes in ClickHouse behavior that explain the boost in performance. We hope you'll enjoy the first step to becoming a ClickHouse performance guru!
Speaker Bio:
Robert Hodges is CEO of Altinity, which offers enterprise support for ClickHouse. He has over three decades of experience in data management spanning 20 different DBMS types. ClickHouse is his current favorite. ;)
The document discusses how data is a strategic asset for organizations and how moving data to the cloud can unlock innovation and accelerate change. It emphasizes that businesses need a data strategy to ensure data is managed and used as an asset. This involves defining requirements for data quality, quantity, and sources. Migrating data to the cloud allows businesses to realize benefits like cost savings, build analytics capabilities, and gain new insights from data lakes, IoT, and machine learning. Case studies provide examples of how companies have benefited from improved data strategies and analytics.
The document provides an introduction to Apache Storm, an open-source distributed real-time computation system. It outlines the core concepts of Storm including topologies, spouts, bolts, streams and tuples. Spouts are sources of streams, while bolts process input streams and produce output streams. Topologies define the logic of an application as a graph of operators and streams. The document also discusses Storm's architecture, guaranteed processing, usage examples at companies like Twitter and Yahoo, and comparisons to other frameworks.
High-Performance Advanced Analytics with Spark-AlchemyDatabricks
Pre-aggregation is a powerful analytics technique as long as the measures being computed are reaggregable. Counts reaggregate with SUM, minimums with MIN, maximums with MAX, etc. The odd one out is distinct counts, which are not reaggregable.
Traditionally, the non-reaggregability of distinct counts leads to an implicit restriction: whichever system computes distinct counts has to have access to the most granular data and touch every row at query time. Because of this, in typical analytics architectures, where fast query response times are required, raw data has to be duplicated between Spark and another system such as an RDBMS. This talk is for everyone who computes or consumes distinct counts and for everyone who doesn’t understand the magical power of HyperLogLog (HLL) sketches.
We will break through the limits of traditional analytics architectures using the advanced HLL functionality and cross-system interoperability of the spark-alchemy open-source library, whose capabilities go beyond what is possible with OSS Spark, Redshift or even BigQuery. We will uncover patterns for 1000x gains in analytic query performance without data duplication and with significantly less capacity.
We will explore real-world use cases from Swoop’s petabyte-scale systems, improve data privacy when running analytics over sensitive data, and even see how a real-time analytics frontend running in a browser can be provisioned with data directly from Spark.
Managing Millions of Tests Using DatabricksDatabricks
Databricks Runtime is the execution environment that powers millions of VMs running data engineering and machine learning workloads daily in Databricks. Inside Databricks, we run millions of tests per day to ensure the quality of different versions of Databricks Runtime. Due to the large number of tests executed daily, we have been continuously facing the challenge of effective test result monitoring and problem triaging. In this talk, I am going to share our experience of building the automated test monitoring and reporting system using Databricks. I will cover how we ingest data from different data sources like CI systems and Bazel build metadata to Delta, and how we analyze test results and report failures to their owners through Jira. I will also show you how this system empowers us to build different types of reports that effectively track the quality of changes made to Databricks Runtime.
Running Mission Critical Workload for Financial Services Institutions on AWSAmazon Web Services
Capital One, Monzo Bank, and the National Australia Bank are examples of financial services customers running mission critical workloads on AWS. AWS provides security, reliability, performance, experience, and scale needed for these workloads. Mission critical workloads require high availability, security, and resilience. On AWS, customers can build fault-tolerant and secure architectures across multiple availability zones for their critical applications and data.
With the rise of the Internet of Things (IoT) and low-latency analytics, streaming data becomes ever more important. Surprisingly, one of the most promising approaches for processing streaming data is SQL. In this presentation, Julian Hyde shows how to build streaming SQL analytics that deliver results with low latency, adapt to network changes, and play nicely with BI tools and stored data. He also describes how Apache Calcite optimizes streaming queries, and the ongoing collaborations between Calcite and the Storm, Flink and Samza projects.
This talk was given Julian Hyde at Apache Big Data conference, Vancouver, on 2016/05/09.
Near Real-Time Data Warehousing with Apache Spark and Delta LakeDatabricks
Timely data in a data warehouse is a challenge many of us face, often with there being no straightforward solution.
Using a combination of batch and streaming data pipelines you can leverage the Delta Lake format to provide an enterprise data warehouse at a near real-time frequency. Delta Lake eases the ETL workload by enabling ACID transactions in a warehousing environment. Coupling this with structured streaming, you can achieve a low latency data warehouse. In this talk, we’ll talk about how to use Delta Lake to improve the latency of ingestion and storage of your data warehouse tables. We’ll also talk about how you can use spark streaming to build the aggregations and tables that drive your data warehouse.
Azure File Share and File Sync guide (Beginners Edition)Naseem Khoodoruth
Azure File Share and File Sync guide (Beginners Edition)
Option to have a file server on premise for caching or access the storage from your local desktop (Windows 10)
#azure #fileserver
As cloud computing continues to gather speed, organizations with years’ worth of data stored on legacy on-premise technologies are facing issues with scale, speed, and complexity. Your customers and business partners are likely eager to get data from you, especially if you can make the process easy and secure.
Challenges with performance are not uncommon and ongoing interventions are required just to “keep the lights on”.
Discover how Snowflake empowers you to meet your analytics needs by unlocking the potential of your data.
Agenda of Webinar :
~Understand Snowflake and its Architecture
~Quickly load data into Snowflake
~Leverage the latest in Snowflake’s unlimited performance and scale to make the data ready for analytics
~Deliver secure and governed access to all data – no more silos
ClickHouse Features for Advanced Users, by Aleksei MilovidovAltinity Ltd
This document summarizes key features for advanced users of ClickHouse, an open-source column-oriented database management system. It describes sample keys that can be defined in MergeTree tables to generate instant reports on large customer data. It also summarizes intermediate aggregation states, consistency modes, and tools for processing data without a server like clickhouse-local.
Why I quit Amazon and Build the Next-gen Streaming SystemYingjun Wu
Yingjun Wu quit their job at Amazon to build a next-generation streaming system called RisingWave that is designed to be cloud-native with a managed service, SQL-oriented interface, and cloud-native architecture in order to reduce operation, development, and performance costs compared to traditional streaming systems. RisingWave aims to provide a streaming database with a simple interface and optimized architecture that fully leverages cloud resources.
HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2017. It is the biggest and most exciting milestone release from the Apache community after 1.0. HBase-2.0 contains a large number of features that is long time in the development, some of which include rewritten region assignment, perf improvements (RPC, rewritten write pipeline, etc), async clients, C++ client, offheaping memstore and other buffers, Spark integration, shading of dependencies as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths.
Speaker
Ankit Singhal, Member of Technical Staff, Hortonworks
Benchmark MinHash+LSH algorithm on SparkXiaoqian Liu
This document summarizes benchmarking the MinHash and Locality Sensitive Hashing (LSH) algorithm for calculating pairwise similarity on Reddit post data in Spark. The MinHash algorithm was used to reduce the dimensionality of the data before applying LSH to further reduce dimensionality and find similar items. Benchmarking showed that MinHash+LSH was significantly faster than a brute force approach, calculating similarities in 7.68 seconds for 100k entries compared to 9.99 billion seconds for brute force. Precision was lower for MinHash+LSH at 0.009 compared to 1 for brute force, but recall was higher at 0.036 compared to vanishingly small for brute force. The techniques were also applied to a real-time streaming
Tableau Tutorial for Data Science | EdurekaEdureka!
YouTube Link:https://youtu.be/ZHNdSKMluI0
Edureka Tableau Certification Training: https://www.edureka.co/tableau-certification-training
This Edureka's PPT on "Tableau for Data Science" will help you to utilize Tableau as a tool for Data Science, not only for engagement but also comprehension efficiency. Through this PPT, you will learn to gain the maximum amount of insight with the least amount of effort.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration.
This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.
Data driven organizations can be challenged to deliver new and growing business intelligence requirements from existing data warehouse platforms, constrained by lack of scalability and performance. The solution for customers is a data warehouse that scales for real-time demands and uses resources in a more optimized and cost-effective manner. Join Snowflake, AWS and Ask.com to learn how Ask.com enhanced BI service levels and decreased expenses while meeting demand to collect, store and analyze over a terabyte of data per day. Snowflake Computing delivers a fast and flexible elastic data warehouse solution that reduces complexity and overhead, built on top of the elasticity, flexibility, and resiliency of AWS.
Join us to learn:
• Learn how Ask.com eliminates data redundancy, and simplifies and accelerates data load, unload, and administration
• Learn how to support new and fluid data consumption patterns with consistently high performance
• Best practices for scaling high data volume on Amazon EC2 and Amazon S3
Who should attend: CIOs, CTOs, CDOs, Directors of IT, IT Administrators, IT Architects, Data Warehouse Developers, Database Administrators, Business Analysts and Data Architects
This document provides an overview of the Elastic Stack including Elasticsearch, Logstash, Kibana, and Beats. It describes how each component works, key terminology, installation and configuration steps. It also demonstrates how to integrate the Elastic Stack for log analytics and security information and event management (SIEM) use cases including sending logs from Auditbeat, configuring file integrity monitoring, and alerting on log events using Elastalert.
Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
Dynamic Partition Pruning in Apache SparkDatabricks
In data analytics frameworks such as Spark it is important to detect and avoid scanning data that is irrelevant to the executed query, an optimization which is known as partition pruning. Dynamic partition pruning occurs when the optimizer is unable to identify at parse time the partitions it has to eliminate. In particular, we consider a star schema which consists of one or multiple fact tables referencing any number of dimension tables. In such join operations, we can prune the partitions the join reads from a fact table by identifying those partitions that result from filtering the dimension tables. In this talk we present a mechanism for performing dynamic partition pruning at runtime by reusing the dimension table broadcast results in hash joins and we show significant improvements for most TPCDS queries.
The JavaScript InfoVis Toolkit provides open source interactive data visualization tools using JSON data formats, including area charts, sunbursts, hyper trees, space trees, and more to help companies visualize hierarchical and quantitative data, though some coding experience is required as it is a library rather than an application.
Stellmach.2011.designing gaze supported multimodal interactions for the explo...mrgazer
This document describes research into designing gaze-supported multimodal interactions for exploring large image collections. Specifically, it investigates combining eye gaze with a touch-and-tilt mobile device or keyboard to control an adaptive fisheye lens visualization. User interviews were conducted to understand how people would want to use such input combinations for browsing images. Based on this feedback, a prototype system was developed using a SpringLens technique with gaze and a touch device. A user study provided insights into how well the gaze-supported interaction techniques were experienced.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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The VISTA project aimed to integrate utility data from various UK organizations to improve coordination and reduce costs of street works. It developed methods for syntactically and semantically integrating heterogeneous utility data through a common data model and global thesaurus. Visualization techniques were also explored that incorporated uncertainty and were driven by an ontology. While the project proved the concept, further work is needed to develop the ontology and address implementation challenges regarding data currency, security, and impact on organizational systems.
This presentation has been prepared by Oleksii Prohonnyi for LvivJS 2015 conference (http://lvivjs.org.ua/)
See the speech in Russian by the following link: https://youtu.be/oi7JhB8eWnA
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IRJET- Saliency based Image Co-SegmentationIRJET Journal
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Similar a Datavisualization - Embed - Focus + Text (20)
International Upcycling Research Network advisory board meeting 4Kyungeun Sung
Slides used for the International Upcycling Research Network advisory board 4 (last one). The project is based at De Montfort University in Leicester, UK, and funded by the Arts and Humanities Research Council.
2. INTRODUCTION
Information visualization is widely acknowledged as a powerful
way of helping users make sense of complicated data, and a
great number of methods for visualizing and working with
various types of information have been presented.
However, all information visualization techniques will have to
comply to one inherent limitation: they will need to limit
themselves to the available area of a computer screen.
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3. INTRODUCTION
A common solution to this problem is to provide some kind of
movable view-port to the data, which can be controlled
through the manipulation of scrollbars or other means.
Zooming interfaces have also been introduced to let users
control the amount of data shown.
Sometimes, however, it might be important to give users access
to both overview and detailed information at the same time;
such techniques include, with separate areas for overview and
detail-on-demand information.
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4. Embed: Focus + Text
Data Visualization
Focus + Text
Integrated Visual Access (EMBED)
Details Overview
Dataset
5. DEFINITION
The family of idioms known as focus+context are based on
the design choice to embed detailed information about a
selected set—the focus—within a single view that also
contains overview information about more of the data—the
context.
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Data Visualization
6. ROLE OF FOCUS+TEXT
These idioms reduce the amount of data to show
in the view through sophisticated combinations of
filtering and aggregation.
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Data Visualization
7. WHY EMBED?
The goal of embedding focus and context together is to
mitigate the potential for disorientation that comes with
standard navigation techniques such as geometric zooming.
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Data Visualization
Geometric zooming allows the user to specify the scale of magnification and
increasing or decreasing the magnification of an image by that scale. This allows the
user focus on a specific area and information outside of this area is generally
discarded. A great example is mapping software like MapQuest.
8. WHY EMBED?
With realistic camera motion, only a small part of world space is visible
in the image when the camera is zoomed in to see details for a small
region. With geometric navigation and a single view that changes over
time, the only way to maintain orientation is to internally remember
one’s own navigation history.
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Data Visualization
Focus+context idioms attempt to support orientation by providing
contextual information intended to act as recognizable landmarks, using
external memory to reduce internal cognitive load.
cognitive
load refers
to the
used
amount of
working
memory
resources
9. ELIDE
One design choice for embedding is elision: some items are omitted
from the view completely, in a form of dynamic filtering.
Other items are summarized using dynamic aggregation for context,
and only the focus items are shown in detail.
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Data Visualization
10. ELIDE - DOITrees (DEGREE OF INTEREST TREES)
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Data Visualization
DOITrees Revisited uses elision to show multiple focus nodes within context in a 600,000 node tree.
12. SUPERIMPOSE
The focus layer is limited to a local region, rather than being a global
layer that stretches across the entire view to cover everything.
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Data Visualization
The superimpose family of design choices pertains to combining multiple
layers together by stacking them directly on top of each other in a single
composite view. Multiple simple drawings are combined on top of each other
into a single shared frame.
13. SUPERIMPOSE
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https://www.megapixl.com/abstract-squares-
superimposed-layers-illustration-56538917
Image Courtesy: saylordotorg.github.io
Data Visualization
14. SUPERIMPOSE
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The superimpose family of design choices pertains
to combining multiple layers together by stacking
them directly on top of each other in a single
composite view. Multiple simple drawings are
combined on top of each other into a single shared
frame.
Data Visualization
15. SUPERIMPOSE
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Tool Glass and Magic Lenses
The Toolglass and Magic Lenses
system uses a see-through lenses
to show color-coded Gaussian
curvature in a foreground layer
consisting of the 3D scene.
Data Visualization
16. SUPERIMPOSE
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The Toolglass and Magic Lenses
idiom provides focus and context
through a superimposed local
layer: the see-through lens color
codes the patchwork
sphere with Gaussian curvature
information and provides a
numeric value for
the point at the center.
Data Visualization
18. DISTORT
In contrast to using elision or layers, many
focus+context idioms solve the problem of
integrating focus and context into a single
view using geometric distortion of the contextual
regions to make room for the details in the focus
regions.
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Data Visualization
19. The Cone Tree system used
3D perspective for
focus+context, providing
a global distortion region
with a single focus point,
and using standard
geometric
navigation for interaction.
DISTORT
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Data Visualization
21. DISTORT - Fisheye Lens
The fisheye lens distortion idiom uses a single focus
with local extent and radial shape and the interaction
metaphor of a draggable lens on top of
the main view.
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Data Visualization
22. DISTORT - Fisheye Lens
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Data Visualization
Focus+context with interactive fisheye lens, with poker player dataset.
(a) Scatterplot showing correlation between two strategies. (b) Dense matrix
view showing correlation between a specific complex strategy and the player’s winning
rate, encoded by color.
24. HYPERBOLIC GEOMETRY
The distortion idiom of hyperbolic geometry uses a single radial global
focus with the interaction metaphor of hyperbolic translation. This
approach
exploits the mathematics of non-Euclidean geometry to elegantly
accommodate structures such as trees that grow by an exponential
factor, in contrast to standard Euclidean geometry where there is only a
polynomial amount of space available for placing items.
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Data Visualization
25. HYPERBOLIC GEOMETRY
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Data Visualization
Animated transition showing
navigation through 3D hyperbolic
geometry for a file system tree laid
out with the H3 idiom, where the
first three frames show hyperbolic
translation changing the focus
point and the last three show
standard
3D rotation spinning the structure
around.
28. STRETCH AND SQUISH NAVIGATION
The stretch and squish navigation idiom uses multiple
rectangular foci of global extent for distortion, and the
interaction metaphor where enlarging some regions causes
others to shrink. In this metaphor, the entire scene is
considered to be drawn on a rubber sheet where stretching
one region squishes the rest.
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Data Visualization
29. PRISequence Juxtaposer
supports comparing gene
sequences using
the stretch and squish
navigation idiom with the
guaranteed visibility of
marks
representing items with a
high importance value, via a
rendering algorithm with
custom subpixel
aggregation.
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Data Visualization
Nonlinear Magnification Fields
30. STRETCH AND SQUISH NAVIGATION
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Data Visualization
Tree Juxtaposer uses stretch and squish navigation with multiple rectangular foci for exploring phylogenetic
trees. (a) Stretching a single region when comparing two small trees. (b) Stretching multiple regions within
a large tree.
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Data Visualization
STRETCH AND SQUISH NAVIGATION
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Data Visualization
Nonlinear Magnification Fields
The nonlinear magnification fields idiom relies on a
general computational framework featuring
multiple foci of arbitrary magnification levels and
shapes, whose scope can be constrained to affect
only local regions.
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Data Visualization
Nonlinear Magnification Fields
General frameworks
calculate the
magnification and
minimization fields
needed to achieve
desired
transformations in the
image. (a) Desired
transformations. (b)
Calculated magnification
fields.
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Data Visualization
Nonlinear Magnification Fields
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Data Visualization
References:
Visualization Analysis & Design
Tamara Munzner
Thank you!